Title and Summary

Director, AI Product Definition & Execution

Who We Are
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible.

Core Services powers Mastercard’s foundational payment platforms, including Consumer Credit, Debit, Prepaid, Commercial, Integrated Processing Solutions, Enterprise Gateway Solutions (EGS), Account Level Management (ALM), MPGS, and Security Services.

Within Core Services, Product Management Technical (PMT) plays a critical role in translating product strategy and business intent into execution-ready work that engineering teams can reliably deliver. This role sits at the intersection of product, engineering, and architecture, and is central to evolving how we scale quality, clarity, and predictability across highly complex, regulated platforms.

Role Overview
We are seeking a director to redefine and modernize the Product Management Technical (PMT) operating model by applying an AI‑first lens to how product intent is shaped, refined, and delivered to engineering.

This is not a traditional product ownership or delivery leadership role. The leader will enter an existing operating model, observe and assess how PMT functions today, and identify systemic sources of ambiguity, rework, and execution of friction leveraging AI and PMT skills to resolve the same.

This leader will redesign how product ideas become buildable software by owning the operating model, the PMT practices, decision frameworks, and AI‑enabled workflows that drive clarity, speed, and predictability across our engineering organization.

Success will be measured not by artifact production, but by improved delivery outcomes—higher backlog readiness, reduced iteration churn, fewer clarification cycles, stronger engineering trust, and more predictable execution across platforms.

The Mission
Transform PMT from a documentation layer into a high-leverage, AI-enabled product definition engine that produces clear, testable, execution ready Features and Stories, reduces ambiguity, improves delivery outcomes, and scales quality through operating mechanisms and AI- Driven Workflows not individual heroics.

Primary Accountabilities
1. Product Definition Operating Model
• Assess the current PMT operating model and identify gaps in clarity, ownership, and decision-making.
• Define and standardize decomposition patterns (Epics 12 Features 12 Stories) that align with how engineering builds and increments value.
• Establish and enforce a consistent Definition of Ready aligned with engineering.
• Standardize acceptance criteria, constraints, and non0functional requirements across platforms.

2. AI-Enabled Product Definition System
• Design how AI is embedded directly into PMT workflows12not as an add0on, but as a core product management capability.
• Use AI to support requirement synthesis, feature and story generation, acceptance criteria creation, and validation.
• Leverage Jira, Confluence, historical delivery data, platform documentation, and architectural signals as structured AI inputs.
• Define prompt frameworks, guardrails, and AI0based quality scoring to ensure outputs meet PMT and engineering standards.

3. Quality System & Feedback Loops
• Define quality metrics owned by PMT, including story quality, readiness scores, and ambiguity indicators leading to PMT Maturity Matrix.
• Track downstream delivery signals such as defects, rework, iteration churn, and delivery delays.
• Build closed0loop feedback mechanisms that continuously connect delivery outcomes back to product definition quality and AI models.

4. PMT Functional Transformation
• Evolve PMT from requirement writers to problem framers, system designers, and decision enablers.
• Upskill PMTs in product thinking, systems thinking, and practical, responsible AI usage.
• Establish shared standards, language, and expectations that scale PMT effectiveness across teams.
• Own the AI enabled PMT functional maturity.

5. Cross-Functional Alignment
• Partner closely with Product, Engineering, and Architecture leaders to align intent, feasibility, constraints, and sequencing.
• Reduce ambiguity and friction at handoffs by improving the clarity and consistency of PMT outputs with AI-enabled decisioning.
• Build trust through transparency, quality signals, and predictable operating mechanisms rather than individual interactions.

Future-State Workflow
• Business intent is collaboratively framed by Product and PMT with explicit outcomes, constraints, and success measures.
• AI0assisted workflows generate Features, Stories, and acceptance criteria aligned to defined standards.
• AI0based quality scoring ensures that Definition of Ready is met prior to engineering intake.
• PMT validates outputs for accuracy, completeness, and delivery of readiness (Strict PMT acceptance)
• Engineering executes with fewer clarification cycles.
• Delivery outcomes continuously feed back into standards, workflows, and AI models.

Success Metrics
- Feature & Story Quality 80%
- 0Backlog readiness 85% across teams
- 0Reduced rework and defects
- Improved delivery predictability
- Increased PMT leverage
- Higher engineering satisfaction

Required Experience
- 10+ years in product, engineering, or technical leadership
- Strong engineering/architecture fluency
- Experience designing operating models
- Hands-on AI usage in workflows
- Systems thinking and ability to scale quality

Preferred Qualifications
- Experience with large-scale platforms and APIs
- Payments or regulated industry experience
- Operating model transformation experience
- Exposure and use of AI-driven tooling

What You Bring
- Engineering-grade rigor in product definition
- AI-first mindset
- Systems thinking
- Leadership in ambiguity
- Executive communication skills